Python 使用 Pandas 读取空格分隔的数据
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/22809061/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Read Space-separated Data with Pandas
提问by Tengis
I used to read my data with numpy.loadtxt(). However, lately I found out in SO, that pandas.read_csv()is much more faster.
我曾经用numpy.loadtxt(). 但是，最近我在SO 中发现，这pandas.read_csv()要快得多。
To read these data I use:
要读取这些数据，我使用：
pd.read_csv(filename, sep=' ',header=None)
The problem that I encounter right now is that in my case the separator can differ from one space, xspaces to even a tab.
我现在遇到的问题是，在我的情况下，分隔符可以从一个空格、x 个空格甚至一个制表符不同。
Here how my data could look like:
我的数据如下所示：
56.00 101.85 52.40 101.85 56.000000 101.850000 1
56.00 100.74 50.60 100.74 56.000000 100.740000 2
56.00 100.74 52.10 100.74 56.000000 100.740000 3
56.00 102.96 52.40 102.96 56.000000 102.960000 4
56.00 100.74 55.40 100.74 56.000000 100.740000 5
That leads to results like:
这会导致如下结果：
0 1 2 3 4 5 6 7 8
0 56 NaN NaN 101.85 52.4 101.85 56 101.85 1
1 56 100.74 50.6 100.74 56.0 100.74 2 NaN NaN
2 56 100.74 52.1 100.74 56.0 100.74 3 NaN NaN
3 56 102.96 52.4 102.96 56.0 102.96 4 NaN NaN
4 56 100.74 55.4 100.74 56.0 100.74 5 NaN NaN
I have to specify that my data are >100 MB. So I can not preprocess the data or clean them first. Any ideas how to get this fixed?
我必须指定我的数据 > 100 MB。所以我不能预处理数据或先清理它们。任何想法如何解决这个问题？
采纳答案by EdChum
Your original line:
您的原始线路：
pd.read_csv(filename, sep=' ',header=None)
was specifying the separator as a single space, because your csvs can have spaces or tabs you can pass a regular expression to the sepparam like so:
将分隔符指定为单个空格，因为您的 csvs 可以有空格或制表符，您可以将正则表达式传递给sep参数，如下所示：
pd.read_csv(filename, sep='\s+',header=None)
This defines separator as being one single white space or more, there is a handy cheatsheet that lists regular expressions.
这将分隔符定义为一个或多个空格，有一个方便的备忘单列出了正则表达式。
